System and method for prosodically modified unit selection databases

ABSTRACT

Systems, methods, and computer-readable storage devices to improve the quality of synthetic speech generation. A system selects speech units from a speech unit database, the speech units corresponding to text to be converted to speech. The system identifies a desired prosodic curve of speech produced from the selected speech units, and also identifies an actual prosodic curve of the speech units. The selected speech units are modified such that a new prosodic curve of the modified speech units matches the desired prosodic curve. The system stores the modified speech units into the speech unit database for use in generating future speech, thereby increasing the prosodic coverage of the database with the expectation of improving the output quality.

PRIORITY INFORMATION

The present application is a continuation of U.S. patent applicationSer. No. 14/275,349, filed May 12, 2014, the content of which isincorporated herein by reference in its entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to improving the quality of a speech unitselection database and more specifically to modifying parts of thespeech unit selection database, then adding the modified speech unitsback into the database for use in future speech generation.

2. Introduction

Speech unit selection synthesis can generate very natural audio outputbut can not be relied upon to produce consistently good audio output.For example, the quality of the speech produced depends highly on thesize and quality of the database of speech samples being used. Toimprove quality, speech selection synthesis can use domain-specificdatabases of speech samples, such that in-domain text for adomain-specific database produces high-quality speech, but resulting inout-of-domain text producing poor quality speech. Previous techniquestend to focus on the segmental level, or repurposing data from othervoices/databases to boost the effective size of a database.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system embodiment;

FIG. 2 illustrates pitch contour templates;

FIG. 3 illustrates a Pitch Synchronous Overlap and Add (PSOLA)conversion;

FIG. 4 illustrates a Residual-Excited Linear Prediction (RELP)conversion;

FIG. 5 illustrates a RELP and PSOLA conversion; and

FIG. 6 illustrates an example method embodiment.

DETAILED DESCRIPTION

A system, method and computer-readable storage devices are disclosedwhich select speech units from a speech unit database, the speech unitscorresponding to text to be converted to speech. Prior techniques do notperform prosody modification, and therefore at times a desired prosodiccontour for a synthetic sentence will be unavailable and substituted bya less satisfactory sequence of units. According to this disclosure, asystem identifies a desired prosodic curve of speech produced fromselected speech units and also identifies an actual prosodic curve ofthe speech units. The selected speech units are modified such that a newprosodic curve of the modified speech units matches the desired prosodiccurve. The system stores the modified speech units into the speech unitdatabase for use in generating future speech, thereby increasing theprosodic coverage of the database with the expectation of improving theoutput quality.

By adding extra data to the speech units database, where the extra datais based on data already in the database but has been transformed usingsignal processing to have a different prosodic realization, the databaseis augmented and the prosodic coverage of the database increases,thereby maintaining or increasing the quality of the resulting speech.One challenge is performing the augmentation without introducingartifacts into the speech units or into the generated speech resultingfrom the modified speech units. This challenge can be met by selectingeffective signal processing techniques. Exemplary signal processingtechniques include Residual-excited Linear Prediction (RELP) and PitchSynchronous Overlap and Add (PSOLA), which can be used alone or withother signal processing techniques (both in series or in parallel).

The signal processing can transform the existing speech units such thatthey have a distinct prosodic realization than previously available.Distinct tasks may require distinct forms of signal processing, wherethe specific attributes of the signal processing in combination with adesired prosodic curve determine which signal processing technique isused. For example, signal processing techniques can require variousamounts of processing time, power, and other logistics to providevarious results (i.e., how the speech units are modified and how thecurves are output) which may be more desirable in certain cases and lessdesirable in other instances.

The first task to be able to generate new prosody from existingutterances. To achieve this, a database is constructed having manyspeech units. Consider the example of a database having speech unitsrecorded from a female speaker speaking American English, where thewoman's speech units are part of a larger collection of speech units.The audio files are 16 kHz, 16 bit audio. The prosody dataset iscomposed of approximately 2100 sentence pairs of the form “CallingRobert Kerr” (a declarative example) and “Was that Robert Kerr?” (aninterrogative example). Each pair (the declarative example and theinterrogative example) uses a different combination of first name andlast name, with one example from each pair of examples has a declarativeintonation and/or a yes/no interrogative intonation. Having a morecomplete sentence context allows the system to produce a more naturalpitch pattern compared to when the speaker pronounces only the names ina statement or question form.

The first and last names are extracted from the prosody dataset based ontheir transcriptions. The extracted names are categorized based on theirsyllable stress pattern. For example, stressed and non-stressedsyllables are marked with “1” and “0”, respectively. From these stresspatterns, stress-pattern classes are developed. The amount of stresspattern classes can be based on pattern recognition, an amount ofprocessing power available, a level of complexity desired (which may bedetermined based on the processing power, or may be predetermined by asystem user), and/or the amount of stress pattern data available. As anexample, 10 stress pattern classes may be identified.

Target prosody templates are trained and prosody hypotheses aregenerated, which together can be used to compare modified speech to adesired stress pattern class. For example, speech units having a firstprosodic curve can be identified as not having a desired prosodic curve.A target prosodic curve can be selected, at which point the systemmodifies the existing stresses of the speech units to match and/ormirror the stresses of the target prosodic curve. When those stressesare within a threshold distance of the target prosodic curve, themodified speech units can be saved/added to a speech unit database.Alternatively, instead of modifying the set of speech units selected,multiple prosody hypotheses can be made from the selected speech units,where each prosody hypothesis represents a modification of the speechunits. After generating the various prosody hypotheses, the systemcompares the prosody hypotheses, selecting the version closest to thetarget prosody template as the desired hypothesis. The desiredhypothesis will then be saved and added to the speech database.

Various embodiments of the disclosure are described in detail below.While specific implementations are described, it should be understoodthat this is done for illustration purposes only. Other components andconfigurations may be used without parting from the spirit and scope ofthe disclosure.

A brief description of a basic general purpose system or computingdevice in FIG. 1 which can be employed to practice the concepts,methods, and techniques disclosed is illustrated. A more detaileddescription of identifying, modifying, and storing prosodic curves willthen follow.

With reference to FIG. 1, an exemplary system and/or computing device100 includes a processing unit (CPU or processor) 120 and a system bus110 that couples various system components including the system memory130 such as read only memory (ROM) 140 and random access memory (RAM)150 to the processor 120. The system 100 can include a cache 122 ofhigh-speed memory connected directly with, in close proximity to, orintegrated as part of the processor 120. The system 100 copies data fromthe memory 130 and/or the storage device 160 to the cache 122 for quickaccess by the processor 120. In this way, the cache provides aperformance boost that avoids processor 120 delays while waiting fordata. These and other modules can control or be configured to controlthe processor 120 to perform various operations or actions. Other systemmemory 130 may be available for use as well. The memory 130 can includemultiple different types of memory with different performancecharacteristics. It can be appreciated that the disclosure may operateon a computing device 100 with more than one processor 120 or on a groupor cluster of computing devices networked together to provide greaterprocessing capability. The processor 120 can include any general purposeprocessor and a hardware module or software module, such as module 1162, module 2 164, and module 3 166 stored in storage device 160,configured to control the processor 120 as well as a special-purposeprocessor where software instructions are incorporated into theprocessor. The processor 120 may be a self-contained computing system,containing multiple cores or processors, a bus, memory controller,cache, etc. A multi-core processor may be symmetric or asymmetric. Theprocessor 120 can include multiple processors, such as a system havingmultiple, physically separate processors in different sockets, or asystem having multiple processor cores on a single physical chip.Similarly, the processor 120 can include multiple distributed processorslocated in multiple separate computing devices, but working togethersuch as via a communications network. Multiple processors or processorcores can share resources such as memory 130 or the cache 122, or canoperate using independent resources. The processor 120 can include oneor more of a state machine, an application specific integrated circuit(ASIC), or a programmable gate array (PGA) including a field PGA.

The system bus 110 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 140 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 100, such as during start-up. The computing device 100further includes storage devices 160 or computer-readable storage mediasuch as a hard disk drive, a magnetic disk drive, an optical disk drive,tape drive, solid-state drive, RAM drive, removable storage devices, aredundant array of inexpensive disks (RAID), hybrid storage device, orthe like. The storage device 160 can include software modules 162, 164,166 for controlling the processor 120. The system 100 can include otherhardware or software modules. The storage device 160 is connected to thesystem bus 110 by a drive interface. The drives and the associatedcomputer-readable storage devices provide nonvolatile storage ofcomputer-readable instructions, data structures, program modules andother data for the computing device 100. In one aspect, a hardwaremodule that performs a particular function includes the softwarecomponent stored in a tangible computer-readable storage device inconnection with the necessary hardware components, such as the processor120, bus 110, display 170, and so forth, to carry out a particularfunction. In another aspect, the system can use a processor andcomputer-readable storage device to store instructions which, whenexecuted by the processor, cause the processor to perform operations, amethod or other specific actions. The basic components and appropriatevariations can be modified depending on the type of device, such aswhether the device 100 is a small, handheld computing device, a desktopcomputer, or a computer server. When the processor 120 executesinstructions to perform “operations”, the processor 120 can perform theoperations directly and/or facilitate, direct, or cooperate with anotherdevice or component to perform the operations.

Although the exemplary embodiment(s) described herein employs the harddisk 160, other types of computer-readable storage devices which canstore data that are accessible by a computer, such as magneticcassettes, flash memory cards, digital versatile disks (DVDs),cartridges, random access memories (RAMs) 150, read only memory (ROM)140, a cable containing a bit stream and the like, may also be used inthe exemplary operating environment. Tangible computer-readable storagemedia, computer-readable storage devices, or computer-readable memorydevices, expressly exclude media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 100, an inputdevice 190 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 170 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 100. The communications interface 180generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic hardware depicted may easily be substituted forimproved hardware or firmware arrangements as they are developed.

For clarity of explanation, the illustrative system embodiment ispresented as including individual functional blocks including functionalblocks labeled as a “processor” or processor 120. The functions theseblocks represent may be provided through the use of either shared ordedicated hardware, including, but not limited to, hardware capable ofexecuting software and hardware, such as a processor 120, that ispurpose-built to operate as an equivalent to software executing on ageneral purpose processor. For example the functions of one or moreprocessors presented in FIG. 1 may be provided by a single sharedprocessor or multiple processors. (Use of the term “processor” shouldnot be construed to refer exclusively to hardware capable of executingsoftware.) Illustrative embodiments may include microprocessor and/ordigital signal processor (DSP) hardware, read-only memory (ROM) 140 forstoring software performing the operations described below, and randomaccess memory (RAM) 150 for storing results. Very large scaleintegration (VLSI) hardware embodiments, as well as custom VLSIcircuitry in combination with a general purpose DSP circuit, may also beprovided.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system 100 shown in FIG. 1 can practice allor part of the recited methods, can be a part of the recited systems,and/or can operate according to instructions in the recited tangiblecomputer-readable storage devices. Such logical operations can beimplemented as modules configured to control the processor 120 toperform particular functions according to the programming of the module.For example, FIG. 1 illustrates three modules Mod1 162, Mod2 164 andMod3 166 which are modules configured to control the processor 120.These modules may be stored on the storage device 160 and loaded intoRAM 150 or memory 130 at runtime or may be stored in othercomputer-readable memory locations.

One or more parts of the example computing device 100, up to andincluding the entire computing device 100, can be virtualized. Forexample, a virtual processor can be a software object that executesaccording to a particular instruction set, even when a physicalprocessor of the same type as the virtual processor is unavailable. Avirtualization layer or a virtual “host” can enable virtualizedcomponents of one or more different computing devices or device types bytranslating virtualized operations to actual operations. Ultimatelyhowever, virtualized hardware of every type is implemented or executedby some underlying physical hardware. Thus, a virtualization computelayer can operate on top of a physical compute layer. The virtualizationcompute layer can include one or more of a virtual machine, an overlaynetwork, a hypervisor, virtual switching, and any other virtualizationapplication.

The processor 120 can include all types of processors disclosed herein,including a virtual processor. However, when referring to a virtualprocessor, the processor 120 includes the software components associatedwith executing the virtual processor in a virtualization layer andunderlying hardware necessary to execute the virtualization layer. Thesystem 100 can include a physical or virtual processor 120 that receiveinstructions stored in a computer-readable storage device, which causethe processor 120 to perform certain operations. When referring to avirtual processor 120, the system also includes the underlying physicalhardware executing the virtual processor 120.

Having disclosed some components of a computing system, the disclosurenow turns to FIG. 2, which illustrates pitch contour templates 200 forthree different distinct stress patterns 206, 208, 210. Each pitchcontour is measured in by the fundamental frequency F_(o) 202 and thefundamental frequency F_(o) template data points 204. The system willidentify the prosodic curve of the speech units for a word beinggenerated, then compare the prosodic curve to the prosodic curves of adesired intonation, accent, or other desired characteristic. Followingthe comparison, the selected speech units will be modified to moreclosely resemble the desired prosodic curve, at which time the modifiedspeech units will be stored.

Consider if the system were to select speech units corresponding to lineA 206. The selected speech units have an overall length, which can be inunits of time or datapoints, and frequencies corresponding to thefundamental frequencies 202 illustrated. However, both the illustratedfrequency F_(o) 202 and the illustrated fundamental frequency F_(o)template data points 204 are for illustration only, and can haveadditional and/or different frequencies from those listed, as well asmore or less data points from those illustrated.

The system has speech templates corresponding to alternative intonationsB 208 and C 210, and determines the selected speech units A 206 shouldbe modified to more closely match the prosodic curve of intonation B208. The system can “stretch” or “shrink” the template data viainterpolation or sampling, depending on the size of the template incomparison to the size of the sampled speech units A. The F_(o) templatedata points 204 can be in the form of time, data points, syllables, orother quantitative measurement relative to the overall expression. As anexample of stretching the template, if a selected word to be generatedwill have a duration of 500 ms and a desired template 208 has a lengthof 250 ms, the template can be extended to a length of 500 ms. Suchextension can require the addition of data points, temporarily orpermanently, to the template. Alternatively, the modified template canbe saved in addition to the original template.

When extending and shrinking the template, the system can perform ananalysis to ensure the extended/shrunken template will provide thedesired alternative. As an alternative to extension or shortening of thetemplate, the system can have many templates of varying lengths, varyingdensity of data points, and varying frequency ranges, from which thetemplates can be selected. Modified templates can be added to the set oftemplates for use in future selection.

The shape of the pitch contours largely depends on the stress patternsof the word/name(s) being synthesized. Different words or names withsimilar stress patterns have similar pitch contours. The interrogativetraining examples can be categorized according to their stress pattern,with an estimated average pitch contour established for each category.To estimate the average pitch contour for each stress category, thesystem can generate a pitch mark for all interrogative training examplesusing a Residual-Excited Linear Prediction (RELP) algorithm and form apitch vector from pitch duration values.

In both configurations, each set of selected speech units has anestimated template pitch contour determined for the interrogative form.The system can rank the pitch vectors of each stress category based ontheir length and choose the median pitch vector as the class's referencepitch vector. Dynamic time warping can be applied on all pitch vectorsin order to align them with the reference pitch vector. The mean of thealigned pitch vectors is computed, which is not a smooth representationof a pitch template contour due to spontaneous errors in the pitch marksand the dynamic time warping performance. The system then performs onedimensional median filtering on the mean pitch vector to generate asmooth pitch template contour, and in this way an interrogative pitchcontour for every stress-pattern is generated.

FIG. 3 illustrates a Pitch Synchronous Overlap and Add (PSOLA)conversion 300. In this example, the speech units selected have adeclarative intonation 306. The speech units having the declarativeintonation 306 and a pitch template 304 for a desired intonation areinputs for a PSOLA pitch modification algorithm 302, resulting in outputspeech units having an interrogative 308 intonation. The interrogativepitch contour can be represented as a vector of pitch values.

As stated above, interpolation can be needed on the pitch template 304such that the summation of all pitch values in the final pitch vector isapproximately equal to the length of the selected speech units. Next,the system aligns the first pitch mark of the pitch template 304 withthe first pitch mark of the selected speech units 306. With the alignedtemplate and speech units, PSOLA is used to modify the pitch of theselected speech units. However, in certain instances when convertingfrom declarative statements to question (interrogative) form, a largechange in pitch value can be present, particularly in the finalsyllables, for which PSOLA is not ideal. In such instances the systemcan use a RELP approach.

FIG. 4 illustrates a Residual-Excited Linear Prediction (RELP)conversion 400 of a declarative statement 406 to an interrogativestatement 418. The system decomposes the speech signal (the selectedspeech units in declarative form) 406 into residual coefficients 410 andLinear Predictive Coder (LPC) coefficients 412. The RELPtechnique/algorithm 408 performs the decomposition, and the pitch marksare extracted from the residual signal 410. The pitch template vector404 is resampled in order to have the same (or close to the same) numberof pitch marks as the residual signal 410, with computing of the ratiosbetween the template pitch values and the pitch values for the selectedspeech units for every adjacent pitch mark. Once aligned, the pitchvalues are scaled 402 so the pitch values associated with the residualsignal 410 is scaled/modified according to the pitch template vector404. The resulting, modified, residual signal 414 is re-sampled usingthe vector of ratio factors, then the signal is “reconstructed” via theRELP algorithm 416 using the LPC coefficients. The resulting output isan interrogative speech output 418. Because RELP can affect higher pitchvalues (toward the end of question formats) by affecting the length ofspeech output, and therefore the local speech rate, compensation can beprovided by resampling the speech and maintaining the duration of theoutput speech 418 more or less equal to the duration of the input speech406.

FIG. 5 illustrates a RELP and PSOLA conversion 500, combining thetechniques described above in FIG. 3 and FIG. 4. In the combinedapproach, instead of applying PSOLA to the selected speech units usingthe template pitch contour 504, the system decomposes the speech signalunits 506 into residual 510 and LPC coefficients 512 using RELP 508.PSOLA pitch modification 502 is then run on the residual coefficients510, modifying them based on the pitch template 504. RELP 516 is usedagain, this time for reconstruction of the signal using the modifiedresidual signal 514 and the original LPC coefficients 512. The advantageof this approach is pitch modification of the residual signal isachieved using a more sophisticated algorithm (PSOLA 502) rather than asimple resampling technique. Also, the number of pitch marks in thepitch template does not necessarily need to be equal to the number ofpitch marks in the original residual 510 and modified residual 514signals. For example, if the PSOLA 502 repeats a frame, a correspondingLPC coefficient can be used. Similarly, if the PSOLA 502 drops a frame,the LPC coefficients for that frame will be ignored. Another advantageof this approach is that since PSOLA 502 is only used to modify theresidual signal 510, the amount of distortion introduced to the finalinterrogative form output 518 is potentially less than the PSOLA-onlybased approach illustrated in FIG. 3.

While RELP and PSOLA are used to describe speech processing algorithms,it is noted that other speech processing algorithms can be used insimilar fashion without detracting from the disclosure. In addition, thealgorithms described are relatively sensitive to the segmental timealignment accuracy of the database from which the selected speech unitsare drawn. The phoneme boundaries for the source and target data are aresult of running forced alignment recognition on the speech data andare relatively (but not completely) accurate. Boundaries that areinaccurate have the potential to affect the quality of the imposed pitchcurves. In practice, while this is a concern and should be monitored,does not seem to strongly impact speech synthesis quality. Where largerportions of a database are being selected and/or diagnosed it could havea larger impact. Also, voiced and unvoiced data are treated differentlyin terms of pitch marks, and therefore accurate matching of voice tovoice frames and unvoiced data to unvoiced frames in both source speechunits and target speech units can require extra monitoring/observation.

Having disclosed some basic system components and concepts, thedisclosure now turns to the exemplary method embodiment shown in FIG. 6.For the sake of clarity, the method is described in terms of anexemplary system 100 as shown in FIG. 1 configured to practice themethod. The steps outlined herein are exemplary and can be implementedin any combination thereof, including combinations that exclude, add, ormodify certain steps.

The system 100 selects speech units from a speech unit database whichcorresponds to text (602) and identifies a desired prosodic curve ofspeech to be produced from the speech units (604). The system 100 alsoidentifies the actual prosodic curve of the speech units selected (606).The desired prosodic curve can correspond to a type of intonation, atype of accent, a level of emotion, a type of emotion, a targetaudience, or other quantifiably distinct prosody. For example, thesystem 100 can identify an actual intonation of speech units as being adeclarative statement when an interrogative intonation is desired. Inparticular, the type of intonation desired can be an interrogativeintonation (such as a yes-no interrogative curve) or a declarativeintonation. As another example, the system 100 can identify the accentof the speech units to be a “Southern” accent when a Chicago accent isdesired. The system 100 can discover the prosody curve of the selectedunits corresponds to an angry intonation/emotion, when a calm, excited,happy, sad, and/or other intonation is desired. As yet another example,the system 100 can identify the prosodic curve as corresponding tosomeone speaking in a “baby voice,” and a desired prosodic curve of anormal voice, elderly voice, educated voice, or other voice type.

The system 100 modifies the speech units such that a new prosodic curvecorresponding to new speech units matches the desired prosodic curve(608). The modification can be a linear, scaling change of pitch, rate,or speed of the selected speech units, or can use speech processingalgorithms such as PSOLA and RELP to modify the speech units. The system100 saves the new, modified speech units to the speech unit database(610). Such a method extend the database of a speech synthesizer byadding pitch-modified units (based on existing units) to the database.These extra units can then be selected and concatenated like any otherspeech units in the database to generate synthesized speech, potentiallyleading to a more natural pitch contour in the synthesized speech. Thesystem 100, in determining if whether to modify speech units or requestthe new, modified speech units from the database, can perform a costanalysis weighing the cost of modifying speech units versus retrievingthe saved speech units from the database. The cost can, for example, bebased on how much processing time/power it a specific function willrequire. For example, an additional step in the illustrated method couldbe generating speech using the modified speech units by accessing themodified database and/or retrieving the modified speech units. Ininstances where the cost of retrieving the speech units is less than thecost of modifying the already selected speech units, the system 100 willretrieve the modified units from the database. Likewise, when the costof retrieving speech units is more than the cost of modifying speechunits, the system 100 can select the more efficient option and modifythe speech units. Such calculations can require the system 100, prior tomodifying the speech units, to perform a calculation to determine if thecost of modifying the selected speech units is higher than a cost ofretrieving the speech units.

Embodiments within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage devices forcarrying or having computer-executable instructions or data structuresstored thereon. Such tangible computer-readable storage devices can beany available device that can be accessed by a general purpose orspecial purpose computer, including the functional design of any specialpurpose processor as described above. By way of example, and notlimitation, such tangible computer-readable devices can include RAM,ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storageor other magnetic storage devices, or any other device which can be usedto carry or store desired program code in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information or instructions are provided via a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readablestorage devices.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Other embodiments of the disclosure may be practiced in networkcomputing environments with many types of computer systemconfigurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. For example, the principles herein apply whenmodifying/analyzing a single set of selected speech units or whenmodifying/analyzing multiple selected speech unit sets simultaneously.Various modifications and changes may be made to the principlesdescribed herein without following the example embodiments andapplications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure. Claim language reciting “atleast one of” a set indicates that one member of the set or multiplemembers of the set satisfy the claim.

We claim:
 1. A method comprising: decomposing, via a residual-excitedlinear prediction algorithm, speech units from a speech unit databaseinto residual coefficients and linear predictive coder coefficients;determining a cost of modifying the residual coefficients to yield adetermination; modifying, via a pitch synchronous overlap and addalgorithm, the residual coefficients, to yield modified residualcoefficients based on the determination; and combining, via theresidual-excited linear prediction algorithm, the modified residualcoefficients with the linear predictive coder coefficients, to yield newspeech units, such that a new prosodic curve corresponding to the newspeech units conforms to a desired prosodic curve, wherein speech can begenerated based on the new speech units.
 2. The method claim 1, furthercomprising: selecting, via a processor, the speech units from a speechunit database, where the speech units are used to generate speechcorrespond to text.
 3. The method claim 1, further comprising:identifying an actual prosodic curve of the speech units.
 4. The methodclaim 1, further comprising: generating speech from the new speechunits.
 5. The method of claim 1, wherein the desired prosodic curvecorresponds to a type of intonation.
 6. The method of claim 5, whereinthe type of intonation is one of a declarative intonation and a yes-nointerrogative.
 7. The method of claim 1, wherein the modifying of theresidual coefficients comprises scaling a pitch of the residualcoefficients.
 8. The method of claim 1, wherein the cost of themodifying of the residual coefficients is higher than a cost ofretrieving the new speech units from the speech unit database.
 9. Asystem comprising: a processor; and a computer-readable storage mediumhaving instructions stored which, when executed by the processor, causethe processor to perform operations comprising: decomposing, via aresidual-excited linear prediction algorithm, speech units from a speechunit database into residual coefficients and linear predictive codercoefficients; determining a cost of modifying the residual coefficientsto yield a determination; modifying, via a pitch synchronous overlap andadd algorithm, the residual coefficients, to yield modified residualcoefficients based on the determination; and combining, via theresidual-excited linear prediction algorithm, the modified residualcoefficients with the linear predictive coder coefficients, to yield newspeech units, such that a new prosodic curve corresponding to the newspeech units conforms to a desired prosodic curve, wherein speech can begenerated based on the new speech units.
 10. The system claim 9, whereinthe computer-readable storage medium stores additional instructionswhich, when executed by the processor, cause the processor to performoperations further comprising: selecting, via a processor, the speechunits from a speech unit database, where the speech units are used togenerate speech correspond to text.
 11. The system claim 9, wherein thecomputer-readable storage medium stores additional instructions which,when executed by the processor, cause the processor to performoperations further comprising: identifying an actual prosodic curve ofthe speech units.
 12. The system claim 9, wherein the computer-readablestorage medium stores additional instructions which, when executed bythe processor, cause the processor to perform operations furthercomprising: generating speech from the new speech units.
 13. The systemof claim 9, wherein the desired prosodic curve corresponds to a type ofintonation.
 14. The system of claim 13, wherein the type of intonationis one of a declarative intonation and a yes-no interrogative.
 15. Thesystem of claim 9, wherein the modifying of the residual coefficientscomprises scaling a pitch of the residual coefficients.
 16. The systemof claim 9, wherein the cost of the modifying of the residualcoefficients is higher than a cost of retrieving the new speech unitsfrom the speech unit database.
 17. A non-transitory computer-readablestorage device having instructions stored which, when executed by acomputing device, cause the computing device to perform operationscomprising: decomposing, via a residual-excited linear predictionalgorithm, speech units from a speech unit database into residualcoefficients and linear predictive coder coefficients; determining acost of modifying the residual coefficients to yield a determination;modifying, via a pitch synchronous overlap and add algorithm, theresidual coefficients, to yield modified residual coefficients based onthe determination; and combining, via the residual-excited linearprediction algorithm, the modified residual coefficients with the linearpredictive coder coefficients, to yield new speech units, such that anew prosodic curve corresponding to the new speech units conforms to adesired prosodic curve, wherein speech can be generated based on the newspeech units.
 18. The non-transitory computer-readable storage deviceclaim 17, wherein the non-transitory computer-readable storage devicestores additional instructions stored which, when executed by thecomputing device, cause the computing device to perform operationsfurther comprising: selecting, via a processor, the speech units from aspeech unit database, where the speech units are used to generate speechcorrespond to text.
 19. The non-transitory computer-readable storagedevice claim 17, wherein the non-transitory computer-readable storagedevice stores additional instructions stored which, when executed by thecomputing device, cause the computing device to perform operationsfurther comprising: identifying an actual prosodic curve of the speechunits.
 20. The non-transitory computer-readable storage device claim 17,wherein the non-transitory computer-readable storage device storesadditional instructions stored which, when executed by the computingdevice, cause the computing device to perform operations furthercomprising: generating speech from the new speech units.